1. Technical Field
Example embodiments of the present invention relate in general to a method of building a map of probability distribution, and more specifically, to a method and apparatus for building a map of probability distribution based on the properties of objects and a system.
2. Related Art
In general, a robot moving indoors and outdoors collects information about the surrounding environment using various kinds of sensors, and builds a map based on the collected information about the surrounding environment. Also, the robot finds an optimal path based on the map, and moves along the optimal path in dynamic and uncertain environments.
For driving of self-driving cars or robots, studies into a method of building a map in which information about the surrounding environment is reflected have been conducted, and as a result of such studies, various map building methods have been proposed.
As methods for building a map in which information about the surrounding environment is reflected, there are a vector field histogram (VFH), a potential field method (PFM), and a probabilistic threat exposure map (PTEM), etc.
The VFH is a real-time path planning algorithm proposed by J. Borenstein. The VFH utilizes a statistical representation of the surrounding environment through the so called histogram grid to represent obstacles.
Also, the VFH deals with uncertainty from sensors and modeling errors, and takes the dynamics and shape of the robot into account, thereby producing sear optimal paths. Lately, the VFH was updated to VFH* through VFH+ to overcome the disadvantages of conventional local path planning algorithms that could not globally ensure optimality.
The PFM is based on finding a target using given information.
In detail, the PFM is an algorithm of producing optimal paths by representing objects on a map in the form of phases, designating the lowest phase as a target, and increasing the phases of obstacles using repulsion.
The PFM has an advantage that it requires a small amount of computation, hot also has a disadvantage that a robot, etc. may fail to produce paths due to local minima. Here, the local minima are cases in which a robot etc. can no longer produce paths because it is stuck in a certain place. For example, the local minimum is a state in which a robot navigating a terrain with uphill and downhill slopes tails into a puddle and can no longer move to another place.
The PTEM is to represent various factors threatening a robot on a map using a Gaussian distribution, and produce optimal paths in real time using the map with the Gaussian distribution in an environment in which various threats exist so that an artificial intelligent apparatus, such as a robot or a self-driving car, can move in teal time.
Meanwhile, conventional methods of building an obstacle map are based on a bottom-up method of recognizing ail objects only as obstacles, regardless of the kinds, etc. of the objects and without taking the kinematic properties of the objects into account, and displaying the objects with different properties as objects with the same property on a map.
Accordingly, since the conventional methods of building the obstacle map do not show dynamical information that can be recognized through tracking of obstacles on the map, it is difficult to accurately calculate times at which an artificial intelligent apparatus arrives obstacles, which also makes it difficult to build an accurate safety map.
Also, since the conventional methods of building the obstacle map represent obstacles only as objects with the same property without taking into account the properties of the objects, for example, the properties of pedestrians, vehicles, two-wheeled vehicles, walls, etc., the built map has low reliability.
Accordingly, since the map and paths produced by the conventional methods of building the obstacle map cannot be determined to be optimal and do not include information such as times at which a robot, etc. arrive obstacles, a system period, etc., it is easy for a robot, etc. to get stack due to a local minimum.